Libraries like Pandas

Pandas Data Structures

Pandas primarily uses two data structures: Series and DataFrame.

• Series: A one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The labels are collectively called the index.

import pandas as pd

data = {'a': 10, 'b': 20, 'c': 30}
series = pd.Series(data)
print(series)

• DataFrame: A two-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects. It has both a row and column index.

data = {'col1': 1, 2, 3, 'col2': 4, 5, 6, 'col3': 7, 8, 9]]}
df = pd.DataFrame(data)
print(df)

# Accessing data
print(df['col1']]) # Accessing a column
print(df.iloc[0]]) # Accessing a row by integer location
print(df.loc[0, 'col1']]) # Accessing a specific cell by label

Series Object (DataFrame Object)

• Index: Both Series and DataFrame use indexes to access data efficiently. Indexes can be integers (default), dates, or even custom objects. Understanding indexes is crucial for efficient data manipulation. Pandas Indexing

• Data Input/Output: Pandas provides excellent tools for reading and writing data from various formats (CSV, Excel, SQL databases, etc.). Pandas I/O

• Data Cleaning and Manipulation: Pandas offers a rich set of functions for cleaning and manipulating data, including handling missing values, data type conversion, and string manipulation. Pandas Data Cleaning

• Data Wrangling: This involves transforming and preparing data for analysis. Includes tasks like merging, joining, pivoting, and reshaping data. Pandas Data Wrangling